Advanced automatic detection of fetal body movements from multichannel magnetocardiographic signals

Both heart rate (HR) monitoring and detection and description of fetal movements provide essential information of the integrity of in utero development and fetal wellbeing. Our previously described method to identify movements from multichannel magnetocardiographic (MCG) recordings lacks of reliability in some cases. This work is aimed at the improvement of fetal movement detection by means of an advanced signal processing and validation strategy.

The previously proposed methodology of fetal body movement detection from MCG recordings using single space angle (SSA), min-max amplitude (MMA) and a measure of the overall signal strength across (RSS) was extended by moving correlation coefficient (MCC). The methodology was developed with respect to the discrimination between active and quiet sleep, validated by testing its coupling with HR accelerations in a total of 137 recordings lasting 30 min from 98 fetuses aged 34-38 weeks of gestation (WGA) of normal pregnancy.

The developed algorithm improves the reliable automatic detection of fetal body movements independent of the fetal sleep states and their changes in the individual MCG recordings. In the fetuses aged 34-38 WGA 94% of 15  ×  15 HR accelerations were coupled with detected movements. The visual inspection of the movement graphs of 30 fetuses aged 20-32 WGA supports the transferability of the movement detector to this age. In four subjects MCG-based movement detection and maternal report on percepted fetal movements were consistent.

The presented methodology allows the parallel automatic acquisition of precise fetal heart rate variability (HRV) indices based on subsequent beat intervals and of fetal body movements from MCG recordings during late 2nd and 3rd trimester. Potential advantages of parallel monitoring of fetal HRV and movements using MCG compared to established ultrasound technology should be investigated in subsequent studies with respect to the identification of fetuses at risk.